Facilitating machine learning configuration

ABSTRACT

Techniques and solutions are described for facilitating the use of machine learning techniques. In some cases, filters can be defined for multiple segments of a training data set. Model segments corresponding to respective segments can be trained using an appropriate subset of the training data set. When a request for a machine learning result is made, filter criteria for the request can be determined and an appropriate model segment can be selected and used for processing the request. One or more hyperparameter values can be defined for a machine learning scenario. When a machine learning scenario is selected for execution, the one or more hyperparameter values for the machine learning scenario can be used to configure a machine learning algorithm used by the machine learning scenario.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a divisional application of U.S. patent application Ser. No.16/837,518, filed Apr. 1, 2020, which is hereby incorporated herein byreference.

FIELD

The present disclosure generally relates to machine learning techniques.Particular implementations relate to configuring machine learningalgorithms for particular use cases.

BACKGROUND

Machine learning is increasingly being used to make, or help make,various decisions, or to otherwise analyze data. Machine learningtechniques can be used to analyze data more quickly or accurately thancould be performed by a human In some cases, it can be impracticable forhumans to manually analyze a data set. Thus, machine learning hasfacilitated the rise of “big data,” by providing ways that such data canbe put to practical use.

However, even for experts in the field, machine learning can becomplicated to understand, including configuring or managing machinelearning models, such as determining when a model should be updated orretrained. The situation can be even more complex when machine learningis applied to particular applications in particular fields. That is, acomputer scientist may understand the algorithms used in a machinelearning technique, but may not understand the subject matter domainwell enough to ensure that a model is accurately trained or to properlyevaluate results provided by machine learning. Conversely, a domainexpert may be well versed in a given subject matter area, but may notunderstand how the machine learning algorithms work.

Software companies have attempted to address these issues by providingpre-configured machine learning scenarios for particular solutions.However, among other things, the accuracy of these “out of the box”solutions can be suboptimal, since they may not be optimized forparticular use cases. Accordingly, room for improvement exists.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

Techniques and solutions are described for facilitating the use ofmachine learning techniques. In some cases, filters can be defined formultiple segments of a training data set. Model segments correspondingto respective segments can be trained using an appropriate subset of thetraining data set. When a request for a machine learning result is made,filter criteria for the request can be determined and an appropriatemodel segment can be selected and used for processing the request. Oneor more hyperparameter values can be defined for a machine learningscenario. When a machine learning scenario is selected for execution,the one or more hyperparameter values for the machine learning scenariocan be used to configure a machine learning algorithm used by themachine learning scenario.

In one aspect, a method is provided for training multiple machinelearning model segments and routing a machine learning request to anappropriate model segments. A selection of at least a first filter typeis received. The selection can be, for instance, user input provided bya key user through a configuration user interface. The at least thefirst filter is applied to a first training data set to produce a firstfiltered training data set.

A machine learning algorithm is trained with the first filtered trainingdata set to provide a first model segment. The machine learningalgorithm is trained with at least a portion of the first training dataset to provide a second model segment. The at least the portion of thefirst training data set is different than the first filtered trainingdata set.

A request is received for a machine learning result, such as from an enduser application, which can be received through an API. It is determinedthat the request includes at least a first filter value. Based at leastin part on the at least the first filter value, the first model segmentor the segment model segment is selected to provide a selected modelsegment. A machine learning result is generated using the selected modelsegment. The machine learning result is returned in response to therequest.

In another aspect, a method is provided for configuring a machinelearning model using one or more hyperparameters. The configuration canbe carried out for use in training a machine learning model, or can beused in generating a machine learning result using a trained model. Userinput is received specifying a first value for a first hyperparameter ofa machine learning algorithm. The first value is stored in associationwith a first machine learning scenario. A first request is received fora machine learning result using the first machine learning scenario. Thefirst value is retrieved. The first machine learning algorithm isconfigured with the first value. A machine learning result is generatedusing the machine learning algorithm configured with the first value.

In a further aspect, a method is provided for processing a request for amachine learning result. A request for a machine learning result isreceived. A machine learning scenario associated with the request isdetermined. At least one value is determined for at least onehyperparameter for a machine learning algorithm associated with themachine learning scenario. The machine learning algorithm is configuredwith the at least one value. At least one filter value specified in therequest is determined. A model segment of a plurality of model segmentsuseable in processing the request is determined, based at least in parton the at least one filter value. A machine learning result is generatedusing the model segment configured with the at least one filer value.

The present disclosure also includes computing systems and tangible,non-transitory computer readable storage media configured to carry out,or including instructions for carrying out, an above-described method.As described herein, a variety of other features and advantages can beincorporated into the technologies as desired.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a computing architecture having a local systemand a cloud system, where each system can provide machine learningfunctionality.

FIG. 2 is a diagram of an example machine learning scenario having modelsegments.

FIG. 3 is a diagram of an example machine learning scenario havingcustomized hyperparameters.

FIG. 4 is a timing diagram illustrating a process for training a machinelearning model with multiple model segments, and use thereof.

FIG. 5 is an example virtual data model definition of a view thatincludes a specification of machine learning model segments.

FIGS. 6-11 are example user interface screens allowing a user toconfigure a machine learning model, including model segments and customhyperparameters.

FIG. 12 is a flowchart illustrating an example method for trainingmultiple segments of a machine learning model, and use thereof.

FIG. 13 is a flowchart illustrating an example method of defining acustom hyperparameter for a machine learning model, and use thereof.

FIG. 14 is a flowchart illustrating an example method of processing arequest for a machine learning result using a model segment appropriatefor a filter specified in the request and a custom hyperparameter.

FIG. 15 is an example processing pipeline for a machine learningscenario.

FIG. 16 is an example table of metadata that can be used in an examplemachine learning scenario that can use disclosed technologies.

FIG. 17 is a schematic diagram illustrating relationships between tableelements that can be included in a data dictionary, or otherwise used todefine database tables.

FIG. 18 is a schematic diagram illustrating components of a datadictionary and components of a database layer.

FIG. 19 is a diagram of an example computing system in which somedescribed embodiments can be implemented.

FIG. 20 is an example cloud computing environment that can be used inconjunction with the technologies described herein.

DETAILED DESCRIPTION Example 1—Overview

Machine learning is increasingly being used to make, or help make,various decisions, or to otherwise analyze data. Machine learningtechniques can be used to analyze data more quickly or accurately thancould be performed by a human In some cases, it can be impracticable forhumans to manually analyze a data set. Thus, machine learning hasfacilitated the rise of “big data,” by providing ways that such data canbe put to practical use.

However, even for experts in the field, machine learning can becomplicated to understand, including configuring or managing machinelearning models, such as determining when a model should be updated orretrained. The situation can be even more complex when machine learningis applied to particular applications in particular fields. That is, acomputer scientist may understand the algorithms used in a machinelearning technique, but may not understand the subject matter domainwell enough to ensure that a model is accurately trained or to properlyevaluate results provided by machine learning. Conversely, a domainexpert may be well versed in a given subject matter area, but may notunderstand how the machine learning algorithms work.

Software companies have attempted to address these issues by providingpre-configured machine learning scenarios for particular solutions.However, among other things, the accuracy of these “out of the box”solutions can be suboptimal, since they may not be optimized forparticular use cases. Accordingly, room for improvement exists.

The present disclosure provides technologies for customizing machinelearning solutions. In one aspect, the present disclosure providestechnologies for developing a plurality of machine learning models fordifferent use cases for a particular data set. As an example, apre-configured, or “out of the box,” machine learning solution may traina machine learning model using a particular data set, but a user maywish to obtain results for input that represents a different data set,which in some cases can be a subset of the type of data used to train amachine learning model. Consider the example of sales data and salesforecasting. If a machine learning model was trained using data forglobal sales, a request to obtain a result (or inference) for aparticular region, such as a particular continent, country, or state,may lead to less accurate results than could be achieved using a modeltrained with a subset of data that be more relevant to the inferencerequest. Consider a forecast for sales of cars with manualtransmissions, if a model were trained using data from countries wheremanual transmissions are common, such as European countries, a requestfor a forecast of sales for cars having manual transmissions within theUnited States, where such cars are much less common, could be quiteinaccurate.

Accordingly, disclosed technologies allow different model segments to becreated for a machine learning scenario, including based on a singletraining data set. A key user (e.g., a user having sufficient knowledgeto configure machine learning scenarios for use by end users) can definecriteria, such as filters, that segment a training data set into one ormore subsets for which machine learning model segments will be created.A request for a machine learning result can be processed using a modelsegment that would be expected to provide the most accurate results. Insome cases, models provided using disclosed technologies can be one ormore subsets of a main data set, and a model for the main data set neednot be made available. In other cases, a main data set can be madeavailable in addition to models corresponding to subsets of the maindata set.

Once a key user has defined what models should be made available, amachine learning framework can train the appropriate models and storethe models for use. When an end user submits a request for aninterference (i.e., a machine learning result for a particular set ofinput data, which can be different than data used to train the model orcan include all or a portion of training data), the machine learningframework can analyze the request to determine the appropriate modelsegment to be used. In some cases, particular filters can be presentedto a user that correspond to available models, to help ensure that amodel is available be used with an end user's request. However, in othercases, the types of inference requests that can be submitted by endusers can be unconstrained, or less constrained. If a model segment isnot found that suitably corresponds to an inference request, an errormessage can be presented to a user. Or, if a “custom” model does notexist, a default model (e.g., using an entire training data set) can beused. Or, if filters or filter values are hierarchically organized, thehierarchy can be traversed towards it root, and the most specific modelsegment that was trained using relevant training data can be selectedfor use. In the case where a default model is used, a user can beprovided with a warning that the results may be less accurate.

Machine learning models are often associated with various settings, atleast some of which can be specified by a user for a particular model.These settings, which can also be referred to as hyperparameters, can beused to help “tune” a model for a particular purpose, which can increasethe accuracy or usefulness of the results. As an example, C and sigmaare hyperparameters for a support vector machines model, while k is ahyperparameter for a k-nearest neighbors model.

For out of the box machine learning solutions, default setting valuescan be provided. The present disclosure allows a user, such as a keyuser, to specify values for one or more settings for a machine learningmodel. When an inference is requested from a machine learning model, amachine learning framework can determine whether any custom settingshave been specified for the model (including for a particular use casefor the model). If so, the custom settings can be applied when producinga machine learning result. Providing for the use of custom settings withmachine learning models that have at least some preconfigured aspectscan be useful, as a user can improve the accuracy of machine learningresults for particular use cases without having to entirely implement amachine learning model. Similarly, allowing for the use of customsettings can allow model settings to be easily updated, and can allow abase model to be easily customized for a variety of use cases.

Other aspects of a machine learning solution, or aspects of othersoftware (e.g., ERP software) that might be used by, or otherwiseaffect, a machine learning solution can be customized for individualusers (or groups of users, such different organizations, or subgroupswithin a given organization). These customizable aspects can includeconfiguration data, which can determine things such as the length ofdata fields (e.g., whether a material ID field is 18 or 40 characters inlength), profiles that should be assigned to data to determine how datashould behave (e.g., providing object-oriented functionality for datathat might not be natively maintained in an object), or rules forautomatically populating at least some data. More generally,configuration data can refer to a specific set of values that aredesired to be used with software that provides for a variety of options.That is, while configuration data does not change an application'ssource code, it can affect application behavior. As with settings,including hyperparameters, default values are typically provided forconfiguration data.

Disclosed technologies provide for storing and applying configurationdata, include configuration data useable with machine learningtechniques. Maintaining configuration data can include transferringconfiguration data between different systems associated with a group ofusers, such as between a test system and a production system. Groups ofusers can be associated with a profile, which can be used to suggestwhat configuration settings are made available to the group. In somecases, some configuration settings might not be relevant to a particulargroup of users, such as because the group is not expected to use certainapplications or application functionality, or because it has beenindicated that default configuration values are appropriate for thegroup of users.

Maintaining configuration data can also be useful in helping to ensurecorrect software operation for a group of users. For example, updatesand upgrades can be evaluated for application depending on whether theymay conflict with a configuration setting, or if the update or upgrademay improve performance associated with a configuration setting (e.g., abug is fixed that is known to occur with a particular value for aparticular configuration setting). Even when updates or upgrades areapplied, storing configuration settings for a group of users cansimplify the update/upgrade process, as prior configuration settings canbe retrieved and applied (e g , manual configuration is not needed),including updating configurations settings as needed based on softwarechanges.

Disclosed technologies can help manage machine learning models. A modelmanagement component can retrain models, such as according to a scheduleor based on model results. In one implementation, model results can bemonitored. If the accuracy of results fails to satisfy a threshold, themodel can be retrained. Similarly, model validation can also beautomated, such as determining whether a model is able to achieve acorrect result for a test data set having a known, desired result.

Example 2—Example Architecture Providing for Machine Learning at Localand Cloud Systems

FIG. 1 illustrates a computing architecture 100 in which disclosedtechnologies can be used. Generally, the architecture 100 includes alocal system 110 and a cloud-based system 114, which can have respectiveclients 116, 118. The local system 110 can include application logic120, which can be logic associated with one or more softwareapplications. The application logic 120 can use the services of a localmachine learning component 122.

The local machine learning component 122 can include one or more machinelearning algorithms, and optionally one or more specific tasks orprocesses. For instance, the local machine learning component 122 canhave functionality for conducting an association rule mining analysis,where the application logic 120 (including as directed by an end user)can call the associated function of the local machine learningcomponent. In carrying out the requested function, the local machinelearning component 122 can retrieve application data 128 from a datastore 126, such as a relational database management system.Alternatively, all or a portion of data to be used by the local machinelearning component 122 be provided to the local machine learningcomponent by the application logic 120, including after being retrievedby, or on behalf of, the application logic from the data store 126.

The application logic 120 can store, or cause to be stored, data in aremote storage repository 132. The remote storage repository 132 can be,for instance, a cloud-based storage system. In addition, oralternatively, the application logic 120 may access data stored in theremote storage repository 132. Similarly, although not shown, in atleast some cases, the local machine learning component 122 may accessdata stored in the remote storage repository 132.

The local system 110 may access the cloud-based system 114 (in whichcase the local system may act as a client 118 of the cloud-basedsystem). For example, one or more components of the cloud-based system114 may be accessed by one or both of the application logic 120 or thelocal machine learning component 122. The cloud-based system 114 caninclude a cloud machine learning component 144. The cloud machinelearning component 144 can provide various services, such as technicalservices 146 or enterprise services 148. Technical services 146 can bedata analysis that is not tied to a particular enterprise use case.Technical services 146 can include functionality for document featureextraction, image classification, image feature extraction, time seriesforecasts, or topic detection. Enterprise services 148 can includemachine learning functionality that is tailored for a specificenterprise use case, such as classifying service tickets and makingrecommendations regarding service tickets.

The cloud system 140 can include predictive services 152. Although notshown as such, in at least some cases the predictive services 152 can bepart of the cloud machine learning component 144. Predictive services152 can include functionality for clustering, forecasting, makingrecommendations, detecting outliers, or conducting “what if” analyses.

Although shown as including a local system 110 and a cloud-based system114, not all disclosed technologies require both a local system 110 anda cloud-based system 114, or innovations for the local system need notbe used with a cloud system, or vice versa.

The architecture 100 includes a machine learning framework 160 that caninclude components useable to implement one or more various disclosedtechnologies. Although shown as separate from the local system 110 andthe cloud system 114, one or both of the local system or the cloudsystem 114 can incorporate a machine learning framework 160. Althoughthe machine learning framework 160 is shown as including multiplecomponents, useable to implement multiple disclosed technologies, agiven machine learning framework need not include all of the componentsshown. Similarly, when both the local system 110 and the cloud system114 include machine learning frameworks 160, the machine learningframeworks can include different combinations of one or more of thecomponents shown in FIG. 1 .

The machine learning framework 160 can include a configuration manager164. The configuration manager 164 can maintain one or more settings166. In some cases, the settings 166 can be used to configure anapplication, such as an application associated with the applicationlogic 120 or with an application associated with the local machinelearning component 122, the cloud machine learning component 144, or thepredictive services 152. The settings 166 can also be used indetermining how data is stored in the data store 126 or a data store 170of the cloud system 114 (where the data store can also store applicationdata 128).

The machine learning framework 160 can include a settings manager 174.The settings manager 174 can maintain settings 176 for use with one orboth of the local machine learning component 122, the cloud machinelearning component 144, or the predictive services 152. As explained inExample 1, the settings 176 can represent hyperparameters for a machinelearning technique, which can be used to tune the performance of amachine learning technique, including for a specific use case.

The machine learning framework 160 can include a model manager 180,which can maintain one or more rules 182. The model manager 180 canapply the rules 182 to determine when a machine learning model should bedeprecated or updated (e.g., retrained). The rules 182 can include rulesthat make a model unavailable or retrain the model using a currenttraining data set according to a schedule or other time-based criterial.The rules 182 can include rules that make a model unavailable or retrainthe model using a current data set based on the satisfaction (or failureto satisfy) non-time based criteria. For example, the model manager 180can periodically examine the accuracy of results provided by a machinelearning model. If the results do not satisfy a threshold level ofaccuracy, the model can be made unavailable for use or retrained. Inanother aspect, the model manager 180 can test a machine learning model,including after the model has been created or updated, to determinewhether the model provides a threshold level of accuracy. If so, themodel can be validated and made available for use. If not, an errormessage or warning can be provided, such as to a user attempting to usethe model.

The machine learning framework 160 can include an inference manager 186.The interference manager 186 can allow a user to configure criteria fordifferent machine learning model segments, which can represent segmentsof a data set (or input criteria, such as properties or attributes thatmight be associated with a data set used with machine learning model). Aconfiguration user interface 188 (also shown as the configuration userinterface 119 of the client system 118) can allow a user (e.g., a keyuser associated with a client 116 or a client 118) to definesegmentation criteria, such as using filters 190. The filters 190 can beused to define model segment criteria, where suitable model segments canbe configured and trained by a model trainer component 192.

Trained models (model segments) 194 (shown as models 194 a, 194 b) canbe stored in one or both of the local system 110 or the cloud system114. The trained models 194 can be models 194 a for particular segments(e.g., defined by a filter 190), or can be models 194 b that are notconstrained by filter criteria. Typically, the models 194 b use atraining data set that is not restricted by criteria defined by thefilters 190. The models 194 b can include models that were not definedusing (or defined for use with) the machine learning framework 160. Themodels 194 b can be used when the machine learning framework 160 is notused in conjunction with a machine learning request, but can also beused in conjunction with the machine learning framework, such as iffilter criteria are not specified or if filter criteria are specifiedbut do not act to restrict the data (e.g., the filter is set to use “alldata”).

The filters 190 can be read by an application program interface 196 thatcan allow users (e.g., end users associated with a client 116 or aclient 118) to request machine learning results (or inferences), wherethe filter 190 can be used to select an appropriate machine learningmodel segment 194 a for use in executing the request. As shown, theclient 116 can include an inference user interface 117 for makinginference requests.

A dispatcher 198 can parse requests received through the applicationprogram interface 196 and route the request to the appropriate modelsegment 194 a for execution.

Example 3—Example Machine Learning Scenarios Providing Model Segmentsand Customizable Hyperparameters

FIG. 2 is a diagram illustrating a machine learning scenario 200 where akey user can define hyperparameters and model segment criteria for amachine learning model, and how these hyperparameters and model segmentscreated using the model segment criteria can be used in inferencerequests by end users. Although shown as including functionality forsetting hyperparameters and model segment criteria, analogous scenarioscan be implemented that include functionality for hyperparameters, butnot model segment criteria, or which include functionality for modelsegment criteria, but not hyperparameters.

The machine learning scenario 200 includes a representation of a machinelearning model 210. The machine learning model 210 can represent amachine learning model 194 of FIG. 1 . The machine learning model 210 isbased on a particular machine learning algorithm. As shown, the machinelearning model 210 is a linear regression model associated with afunction (or algorithm) 218. In some cases, the machine learningscenario 200 includes a reference (e.g., a URI for a location of themachine learning model, including for an API for accessing the machinelearning model).

The machine learning model 210 can be associated with one or moreconfiguration settings 222. Consider an example where the machinelearning model 214 is used to analyze patterns in traffic on a computernetwork, including patterns associated with particular geographicregions. A configuration setting 222 can include whether the networkprotocol uses IPv4 or IPv6, as that can affect, among other things, thenumber of characters expected in a valid IP address, as well as the typeof characters (e.g., digits or alphanumeric). In the case where themachine learning model 214 is provided as an “out of the box” solutionfor network traffic analysis, the configuration settings 222 can beconsidered a setting that is not intended to be altered by a key user,and it is a basic setting/parameter for the machine learning model,rather than being used to tune model results.

The machine learning model 214 can further include one or morehyperparameters 226. The hyperparameters 226 can represent parametersthat can be used to tune the performance of a particular machinelearning model. One hyperparameter is an optimizer 228 that can be usedto determine values for use in the function 218 (e.g., for w). As shown,the gradient descent technique has been selected as the optimizer 228.The optimizer 228 can itself be associated with additionalhyperparameters, such as, η, a learning rate (or step size) 230 and anumber of iterations 232, “n_iter.”

The values of the hyperparameters 226 can be stored, such as in thesettings 166 of the configuration manager 164 of FIG. 1 . Values forhyperparameters 226 can be set, such as by a key user using aconfiguration user interface 234 (which can be the configuration userinterface 119 of FIG. 1 ). The scenario 200 shows hyperparametersettings 238 being sent by the configuration user interface 234 to bestored in association with the regression model 214. In addition tosetting the optimizer to “gradient descent,” the hyperparameterssettings 238 set particular values for η and for the number iterationsto be used.

Particular values for the hyperparameters 226 can be stored in adefinition for the machine learning model 214 that is used for aparticular machine learning scenario 200. For example, a machinelearning scenario 200 can specify the function 218 that should be usedwith the model, including by specifying a location (e.g., a URI) orotherwise providing information for accessing the function (such as anAPI call). The definition can also include values for thehyperparameters 226, or can specify a location from which hyperparametervalues can be retrieved, and an identifier that can be used to locatethe appropriate hyperparameter values (which can be an identifier forthe machine learning model scenario 200). Although a user (or externalprocess) can specify values for some or all of the hyperparameters 226,a machine learning scenario 200 can include default hyperparametersvalues that can be used for any hyperparameters whose values are notexplicitly specified.

One or more filters 250 can be defined for the machine learning scenario200, and can correspond to the filters 190 of FIG. 2 . The filters 250can be used to define what machine learning model segments are created,what machine learning model segments are made available, and criteriathat can be used to determine what machine learning model segment willbe used to satisfy a particular inference request.

FIG. 2 illustrates that filters 250 can have particular types orcategories, and particular values for a given type or category. Inparticular, the machine learning scenario 200 is shown as providingfilters for a region type 254, where possible values 256 for the regiontype include all regions, all of North America, all of Europe, values bycountry (e.g., Germany, United States), or values by state (e.g.,Alaska, Nevada). Although a single filter type is shown, a given machinelearning scenario 200 can include multiple filter types. In the exampleof network traffic analysis, additional filters 250 could include time(e.g., traffic during a particular time of a day), a time period (e.g.,data within the last week), or traffic type (e.g., media streaming) Whenmultiple filter categories are used, model segments can be created forindividual values of individual filters (or particular values selectedby a user) or for combinations of filter values (e.g., streaming trafficin North America), where the combinations can optionally be thoseexplicitly specified by a user (particularly in the case where multiplefilter types and/or multiple values for a given type exist, which canvastly increase the number of model segments).

Model segments 260 can be created using the filters 250. As shown, modelsegments 260 are created for the possible value of the region filtertype 254, including a model segment 260 a that represents an unfilteredmodel segment (e.g., includes all data). In some cases, the modelsegment 260 a can be used as a default model segment, including in aninference request that is received that includes parameters that cannotbe mapped to a more specific model segment 260.

When an end user wishes to request an inference (that is, obtain amachine learning result, optionally included an explanation as to itspractical significance, for a particular set of input data), the usercan select a data set and optionally filters using an application userinterface 264. In at least some cases, filters (both types and possiblevalues) presented in the application user interface 264 correspond tofilters 250 (including values 256) defined for a given machine learningscenario 200 by a key user. Available filters 250, and possibly values256, can be read from a machine learning scenario 200 and used topopulate options presented in the application user interface 264.

In other cases, the application user interface 264 can provide fewer, orno, constraints on possible filter types 254 or values 256 that can berequested using the application user interface 264. When an interferencerequest is sent from the application user interface 264 for processing,a dispatcher 272 can determine one more model segments 260 that may beused in processing the request, and can select a model segment (e.g.,based on which model segment would be expected to provide the mostaccurate or useful results). If no suitable model segment 260 is found,an error can be returned in response to the request. Or a default modelsegment, such as the model segment 260 a, can be used.

The inference request can be sent to an application program interface268, which can be the application program interface 196 of FIG. 1 . Theapplication program interface 268 can accept inference requests, andreturn results, on behalf of the dispatcher 272 (which can be thedispatcher 198 of FIG. 1 ). The dispatcher 272 can determine for arequest received through the API 268 what model segment 260 should beused for the request. The determination can be made based on filtervalues 256 provided using the application user interface 264.

As an example, consider a first inference request 276 that includes afilter value of “North America.” The dispatcher 272 can determine thatmodel segment 260 b matches that filter value and can route the firstinference request 276 to the model segment 260 b for processing (orotherwise cause the request to be processed using the model segment 260b). A second inference request 278 requests that data be used forCalifornia and Nevada. The dispatcher 272 can review the available modelsegments 260 and determine that no model segment exactly matches thatrequest.

The dispatcher 272 can apply rules to determine what model segment 260should be used for an inference request when no model segment exactlymatches request parameters. In one example, model segments 260 can havea hierarchical relationship. For instance, filter types 254 or values256 can be hierarchically organized such that “North America” is knownto be a subset of the “all values” model segment 260 a. Similarly, thefilter values can be organized such that a U.S. state is known to be asubset of “United States,” where in turn “United States” can be a subsetof “North America.” If no model segment 260 matches a given level of afilter hierarchy, the next higher (e.g., more general, or closer to theroot of the hierarchy) can be evaluated for suitability.

For the second inference request 278, it can be determined that, whilesegments models 260 may exist for California and Nevada separately; nomodel exists for both (and only) California and Nevada. The dispatcher272 can determine that a segment model 260 d for “United States” is amodel segment higher in the filter hierarchy that is that most specificmodel segment that includes data for both California and Nevada. Whilethe model segment 260 b for North America also includes data forCalifornia and Nevada, it is less specific than the model segment 260 dfor the United States.

FIG. 3 illustrates a machine learning scenario 300 that is generallysimilar to the machine learning scenario 200 of FIG. 2 and illustrateshow hyperparameter information can be determined for a given inferencerequest. Assume that a user enters an inference request using theapplication user interface 264. Machine learning infrastructure 310,which can correspond to the machine learning framework 160, candetermine whether the inference request is associated with particularhyperparameters values or if default values should be used. Determiningwhether a given inference request is associated with specifichyperparameters can include determining a particular user or processidentifier is associated with specific hyperparameter values.Information useable to determine whether an inference request isassociated with specific hyperparameter values can optionally beincluded in a call to the application program interface 268 (e.g., thecall can include as arguments one or more of a process ID, a user ID, asystem ID, a scenario ID, etc.). If no specific hyperparameter valuesare found for a specific inference request, default values can be used.

There can be advantages to implementations where functionality for modelsegments is implemented independently of functionality forhyperparameters. That is, for example, a given set of trained modelsegments can be used with scenarios with different hyperparameter valueswithout having to change the model segments or a process that uses themodel segments. Similarly, the same hyperparameters can be used withdifferent model segments or interference request types (e.g., a givenset of hyperparameters can be associated with multiple machine learningscenarios 200), so that hyperparameter values do not have to beseparately defined for each model segment/inference request type.

Example 4—Example Process for Training and Use of Machine Learning ModelSegments

FIG. 4 is a timing diagram illustrating an example process 400 fordefining and using model segments. The process 400 can be implemented inthe computing environment 100 of FIG. 1 , and can represent a particularinstance of the scenario 200 of FIG. 2 .

The process 400 can be carried out by an administrator 410 (or, moretechnically, an application that provides administrator functionality,such as to a key user), a training infrastructure 412 (e.g., the machinelearning framework 160 of FIG. 1 ), a training process 414 (e.g.,carried out by the machine learning component 122, the cloud machinelearning component 144, or the predictive services 152 of FIG. 1 ), amodel dispatcher 416 (e.g., the dispatcher 198), an inference API 418(e.g., the API 196), and a machine learning application 420 (e.g., anapplication executing on a client device 116, 118, or a machine learningapplication executing on the local system 110 or the cloud system 114).

Initially, the administrator 410 can define one or more filters at 428.The one or more filters can include one or more filter types, and one ormore filter values for each filter type. In at lease some cases, thefilter types, and values, correspond to attributes of a data set to beused with a machine learning model, or metadata associated with such adata set. In the case where data (input or training) is stored inrelational database tables, the filter types can correspond toparticular table attributes, and the values can correspond to particularvalues found in the data set for those attributes. Or, the filter typescan correspond to a dimensional hierarchy, such as associated with anOLAP cube or similar multidimensional data structure.

The filters defined at 428 are sent to the training infrastructure 412.The training infrastructure 412, at 432, can register the filters inassociation with a particular machine learning model, or a particularscenario (which can have an identifier) that uses the model. Themodel/scenario can be used, for example, to determine which filter (andin some cases filter values) should be displayed to an end user forgenerating an inference request. While in some cases filter values canbe explicitly specified, in other cases they can be populated from adata set based on filter types. For example, if a filter type is“state,” and a data set includes only data for Oregon and Arizona, thosevalues could be provided as filter options, while filter values forother states (e.g., Texas) would not be displayed as options. Anindication that the filter has been defined and is available for use canbe sent from the training infrastructure 412 to the administrator 410.

At 436, the administrator 410 can trigger training of model segmentsusing the defined filter by sending a request to the traininginfrastructure 412. The training infrastructure 412 can use therequested filters to define and execute a training job at 440. Thetraining job is sent to the training process 414. The training process414 filters training data at 444 using the defined filters. The modelsegment is then trained using the filtered data at 448. The segmentmodels are returned (e.g. registered or indicated as active) to thetraining infrastructure 412 by the training process 414 at 452. At 456,the segment models are returned by the training infrastructure 412 tothe administrator 410.

The machine learning application 420 can request an inference at 460.The inference request can include an identification of one or morefilter types, having one more associated filter values. The inferencerequest is sent from the machine learning application 420 to theinference API 418. At 464, the inference API 418 forwards the inferencerequest to the model dispatcher 416. The model dispatcher 416, at 468,determines a model segment to be used in processing the inferencerequest. The determination can be made based on the filter types andvalues included in the inference request from the machine learningapplication 420, and can be carried out as described for the scenario200 of FIG. 2 .

The model dispatcher 416 sends the inference request to the traininginfrastructure 412, to be executed on the appropriate model segment (asdetermined by the model dispatcher). The training infrastructure 412determines a machine learning result, which can include an inferencedrawn from the result, at 476, and sends the result to the modeldispatcher 416, which in turn returns the result at 480 to the API 418,and the API can return the result to the machine learning application420 at 484. The machine learning application 420 can display the machinelearning result, such as to an end user, at 488.

Example 5—Example Data Artefact Including Model Segment Filters

FIG. 5 illustrates an example definition 500 for a data artefact, suchas a data artefact of a virtual data model, illustrating howsegmentation information can be provided. The definition is a Core DataService view definition, as used in products available from SAP SE, ofWalldorf, Germany.

The definition 500 includes code 510 defining data referenced by theview, which can be used to construct a data artefact in a database(e.g., in a data model for the data, such as in an information schema ordata dictionary for a physical data model for the database)corresponding to the view. The definition 500 includes elements 514,516, which are attributes (in this case, non-key attributes) that can beused for model segmentation. In some cases, the elements 514, 516 canrepresent elements that a key user can select for creating modelsegments. In other cases, the elements 514, 516 represent filters thathave been defined for a model, and for which corresponding modelsegments have been created (e.g., using the process 400 of FIG. 4 ).Generally, key or non-key attributes included in the definition 500 canbe used to define model segments.

Example 6—Example User Interface Screens for Configuring MachineLearning Models

FIGS. 6-9 provide a series of example user interface screensillustrating how a machine learning scenario (e.g., a particularapplication of a particular machine learning model) can be configured touse disclosed technologies. The screens can represent screens that areprovided to a key user, such as in the configuration user interface 119of the client 118 of FIG. 1 (or the configuration user interface 234 ofFIG. 2 or FIG. 3 ).

FIG. 6 provides an example user interface screen 600 that allows a userto provide basic definitional information for a machine learningscenario, including entering a name for the scenario in a field 610 anda description for the scenario in a field 612. A field 616 provides atype for the scenario, which can represent a particular machine learningalgorithm that is to be used with the scenario. In some cases, the field616 can be linked to available machine learning algorithms, such that auser may select from available options, such as using a drop down menu.

A package, which can serve to contain or organize development objectsassociated with the machine learning scenario, can be specified in afield 620. In other cases, the package can indicate a particularsoftware package, application, or application component with which thescenario is associated. For example, the value in the field 620 canindicate a particular software program with which the scenario 600 isassociated, where the scenario can be an “out of the box” machinelearning scenario that is available for customization by a user (e.g., akey user).

A status 624 of the scenario can be provided, as can a date 626associated with the status. The status 624 can be useful, such as toprovide an indication as to whether the scenario has already beendefined/deployed and is being modified, or if the scenario is currentlyin a draft state. A user can select whether a scenario is extensible byselecting (or not) a check box 630. Extensible scenarios can bescenarios that are customizable by customers/end users, where extensiblecustomizations are configured to be compatible with any changes/updatesto the underlying software. Extensible scenarios can allow for changesto be made such as changing a machine learning algorithm used with thescenario, extending machine learning logic (such as includingtransformations or feature engineering), or extending a consumption APIfor a model learning model.

One or more data sets to be used with the machine learning scenario canbe selected (or identified) using fields 640, 644, for training data andinference data, respectively.

Once a scenario has been defined/modified, a user can choose to takevarious actions. If a user wishes to discard their changes, they can doso be selecting a cancel user interface control 650. If a user wishes todelete a scenario (e.g., a customized scenario) that has already beencreated, they can do so by selecting a delete user interface control654. If the user wishes to save their changes, but not activate ascenario for use, they can do so by selecting a save draft userinterface control 658. If the user wishes to make the scenario availablefor use, they can do so by selecting a publish user interface control662.

Navigation controls 670 can allow a user to navigate between the screensshown in FIGS. 6-9 , to define various aspects of a scenario. Thescenario settings screen 600 can be accessed by selecting a navigationcontrol 674. An input screen 700, shown in FIG. 7 , can be accessed byselecting a navigation control 676. An output screen 800, shown in FIG.8 , can be accessed by selecting a navigation control 678. A screen 900,shown in FIG. 9 , providing information for models used in the scenario,can be accessed by selecting a navigation control 680.

FIG. 7 presents a user interface screen 700 that allows a user to viewattributes that are used to train a model used for the scenario. In somecases, the attributes are pre-defined for a given scenario, but areexpected to match the training or inference (e.g. input/apply) data setsspecified using the fields 640, 644 of FIG. 6 . In other cases, theattributes are populated based on the data sets specified using thefields 640, 644.

For each attribute, the user interface screen 700 lists the name 710 ofthe field, the data type 714 used by the machine learning modelassociated with the scenario, a data element 718 (e.g., a data elementdefined in a data dictionary and associated with the attribute, where adata element can be a data element as implemented in products availablefrom SAP SE, of Walldorf, Germany) of the source data set (which typecan be editable by a user), details 722 regarding the data type (e.g., ageneral class of the data type, such as character or numerical, amaximum length, etc.), a role 724 for the attribute (e.g., whether itacts as a key, or unique identifier, for data in a data set, serves as anon-key input, or whether it is an attribute whose value is to bepredicted using a machine learning algorithm), and a description 726 forthe attribute.

In a specific implementation, a user may select attributes of the userinterface screen 700 to be used to define model segments. For example, auser may select attribute to be used for model segment definition byselecting a corresponding checkbox 730 for the attribute. In theimplementation shown, attributes selected using checkboxes 730 can beused to define filter types or categories. An underlying data set can beanalyzed to determine particular filter values that will be madeavailable for a given data set. In other cases, the user interfacescreen 700 can provide an input field that allows a user to specifyparticular values for attributes used for model segmentation.

The user interface screen 700 can include the navigation controls 670,and options 650, 654, 658, 662 for cancelling input, deleting ascenario, saving a draft of a scenario, or publishing a scenario,respectively.

The user interface screen 800 can be generally similar to the userinterface screen 700, but is used to provide information, and optionallyconfigure, information for attributes or other values (e.g., machinelearning results) provided as output of a machine learningscenario/model.

The user interface screen 800 displays the name 810 for each attribute,the data type 812 used by the machine learning algorithm, a field 814that lists a data element associated with the attribute (which can beedited by a user), and data type information 816 (which can be analogousto the data type information 722 of FIG. 7 ). The user interface screen800 can also list a role 820 for each attribute as well as a description824 for the attribute. The roles 820 can be generally similar to theroles 724. As shown, the roles 820 can indicate whether the outputattribute identifies a particular record in a data set (including arecord corresponding to a machine learning result), whether theattribute is a target (e.g., that is determined by the machine learningalgorithm, as opposed to being an input value), or whether the result isa predicted value. In some cases, a predicted attribute can be anattribute whose value is determined by a machine learning algorithm andwhich is provided to a user as a result (or otherwise used indetermining a result presented to a user, such as being used todetermine an inference, which is then provided to a user). A targetattribute can be an attribute whose value is determined by a machinelearning algorithm, but which may not be, at least directly, provided toa user. In some cases, a particular data can have multiple roles, andcan be associated with (or listed as) multiple attributes, such as beingboth a target attribute and a prediction attribute.

The user interface screen 800 also shows details 840 for an applicationprogram interface associated with the scenario being defined. Thedetails 840 can be presented upon selection of a user interface control(not shown in FIG. 8 , but which can correspond to a control 780 shownin FIG. 7 ). The details 840 can identify a class (e.g., in an objectoriented programming language) 844 that implements the API and anidentifier 848 for a data artefact in a virtual data model (e.g., theview 500 of FIG. 5 ) that specifies data to be used in generating aninference. In at least some cases, the API identified in the details 840can include functionality for determining a model segment to be usedwith an inference request, or at least accepting such information whichcan be used by another component (such as a dispatcher) to determinewhich model segment should be used in processing a given inferencerequest. The data artefact definition of FIG. 5 can represent an exampleof a data artefact identified by the identifier 848.

The user interface screen 800 can include the navigation controls 670,and options 650, 654, 658, 662 for cancelling input, deleting ascenario, saving a draft of a scenario, or publishing a scenario,respectively.

The user interface screen 900 of FIG. 9 can provide information aboutparticular customized machine learning scenarios that have been createdfor a given “out of the box” machine learning scenario. The userinterface screen 900 can display a name 910 for each model, adescription 912 of the model, and a date 914 the model was created. Auser can select whether a given model is active (e.g., available for useby end users) by selecting a check box 918. A user can select to train(or retrain) one or more models for a given scenario by selecting atrain user interface control 922. Selecting a particular model (e.g., byselecting its name 910) can cause a transition to a different userinterface screen, such as taking the user to the settings user interfacescreen 600 with information displayed for the selected scenario.

Example 7—Example User Interface Screen for Defining Machine LearningModel Segments

FIG. 10 provides another example user interface screen 1000 throughwhich a user can configure filters that can be used to generate modelsegments that will be available to end users for requests for machinelearning results. The user interface screen 1000 can display a name 1010for the overall model, which can be specified in the screen 1000 or canbe populated based on other information. For example, the screen 1000can be presented to a user in response to a selection on another userinterface screen (e.g., the user interface screen 600 of FIG. 6 ) tocreate model segments, and the model name can be populated based oninformation provided in that user interface screen, or another source ofinformation defining a machine learning model or scenario. Similarly,the screen 1000 can display the model type 1014, which can be populatedbased on other information. The screen 1000 can provide a field, or textentry area, 1018 where a user can enter a description of the model, forexplanation purposes to other uses, including criteria for definingmodel segments.

A user can define various training filters 1008 using the screen 1000.Each filter 1008 can be associated with an attribute 1022. In somecases, a user may select from available attributes using a dropdownselector 1026. The available attributes can be populated based onattributes associated with a particular input or training dataset, orotherwise defined for a particular machine learning scenario. Eachfilter 1008 can include a condition type (e.g., equals, between, notequal to) 1030, which can be selected using a dropdown selector 1034.Values to be used with the condition 1030 can be provided in one or morefields 1038. A user may select to add additional filters, or deletefilters, using controls 1042, 1044, respectively.

Once the filters 1008 have be configured, a user can choose to train oneor more model segments using the filters by selecting a train userinterface control 1048. The user can cancel defining model segments byselecting a cancel user interface control 1052.

Example 8—Example User Interface Screen for Defining CustomHyperparameters for a Machine Learning Model

FIG. 11 provides an example user interface screen 1100 through which auser can define hyperparameters to be used with a machine learningmodel. Depending on the machine learning algorithm, the hyperparameterscan be used during one or both of training a machine learning model andin using a model as part of responding to a request for a machinelearning result.

The user interface screen 1100 includes a field 1110 where a user canenter a name for the hyperparameter settings, and a field 1114 where auser can enter a pipeline where the hyperparameter settings will beused. In some cases, a pipeline can represent a specific machinelearning scenario. In other cases, a pipeline can represent one or moreoperations that can be specified for one or more machine learningscenarios. For example, a given pipeline might be specified for twodifferent machine learning scenarios which use the same machine learningalgorithm (or which have at least some aspects in common such that thesame pipeline is applicable to both machine learning scenarios).

For each hyperparameter available for configuration, the user interfacescreen can provide a key identifier 1120 that identifies the particularhyperparameter and a field 1124 where a user can enter a correspondingvalue for the key. The keys and values can then be stored, such as inassociation with an identifier for the pipeline indicated in the field1114. In at least some cases, the hyperparameters available forconfiguration can be defined for particular machine learning algorithmsTypically, while a key user may select values for hyperparameters, adeveloper of a machine learning platform (e.g., the local machinelearning component 122 or the cloud machine learning component 144 orpredictive services 152 of FIG. 1 ) defines what hyperparameters will bemade available for configuration.

Example 9—Example Configuration and Use of Machine Learning ModelsHaving Model Segments and/or Custom Hyperparameters

FIG. 12 is a flowchart of an example method 1200 for training multiplemachine learning model segments and routing a machine learning requestto an appropriate model segments. The method 1200 can be carried outusing the computing architecture 100 of FIG. 1 , and can use a machinelearning scenario 200 as shown in FIG. 2 . The process 400 of FIG. 4 canrepresent a particular example of the method 1200.

At 1204, a selection of at least a first filter type is received. Theselection can be, for instance, user input provided by a key userthrough a configuration user interface. The at least the first filter isapplied to a first training data set to produce a first filteredtraining data set at 1208.

At 1212, a machine learning algorithm is trained with the first filteredtraining data set to provide a first model segment. The machine learningalgorithm is trained at 1216 with at least a portion of the firsttraining data set to provide a second model segment. The at least theportion of the first training data set is different than the firstfiltered training data set.

At 1220, a request is received for a machine learning result, such asfrom an end user application, which can be received through an API. Itis determined at 1224 that the request includes at least a first filtervalue. Based at least in part on the at least the first filter value, at1228, the first model segment or the segment model segment is selectedto provide a selected model segment. At 1232, a machine learning resultis generated using the selected model segment. The machine learningresult is returned at 1236 in response to the request.

FIG. 13 is a flowchart of an example method 1300 for configuring amachine learning model using one or more hyperparameters. Theconfiguration can be carried out for use in training a machine learningmodel, or can be used in generating a machine learning result using atrained model. The method 1300 can be carried out using the computingarchitecture 100 of FIG. 1 , and can use the machine learning scenario300 of FIG. 3 .

At 1304, user input is received specifying a first value for a firsthyperparameter of a machine learning algorithm. The first value isstored at 1308 in association with a first machine learning scenario. At1312, a first request is received for a machine learning result usingthe first machine learning scenario. The first value is retrieved at1316. At 1320, the first machine learning algorithm is configured withthe first value. A machine learning result is generated at 1324 usingthe machine learning algorithm configured with the first value.

FIG. 14 is a flowchart of an example method 1400 for processing arequest for a machine learning result. The method 1400 can be carriedout in the computing architecture 100 of FIG. 1 , and can use themachine learning scenarios 200, 300 of FIGS. 2 and 3 . The process 400shown in FIG. 4 can represent a particular example of at least a portionof the method 1400.

At 1404, a request for a machine learning result is received. A machinelearning scenario associated with the request is determined at 1408. At1412, at least one value is determined for at least one hyperparameterfor a machine learning algorithm associated with the machine learningscenario. The machine learning algorithm is configured at 1416 with theat least one value. At 1420, at least one filter value specified in therequest is determined. A model segment of a plurality of model segmentsuseable in processing the request is determined at 1424, based at leastin part on the at least one filter value. At 1428, a machine learningresult is generated using the model segment configured with the at leastone filter value.

Example 10—Example Machine Learning Pipeline

FIG. 15 illustrates an example of operators in a machine learningpipeline 1500 for a machine learning scenario. The machine learningscenario can represent a machine learning scenario of the typeconfigurable using the user interface screens shown in FIGS. 6-11 , or ascenario 200, 300 depicted in FIGS. 2 and 3 .

The machine learning pipeline 1500 includes a data model extractoroperator 1510. The data model extractor operator 1510 can specifyartefacts in a virtual data model from which data can be extracted. Thedata model extractor operator 1510 typically will include path/locationinformation useable to locate the relevant artefacts, such as anidentifier for a system on which the virtual data model is located, anidentifier for the virtual data model, and identifiers for the relevantartefacts.

The data model extractor operator 1510 can also specify whether dataupdates are desired and, if so, why type of change data processingshould be used, such as whether timestamp/date based change detectionshould be used (and a particular attribute to be monitored) or whetherchange data capture should be used, and how often updates are requested.The data model extractor operator 1510 can specify additionalparameters, such as a package size that should be used in transferringdata to the cloud system (or, more generally, the system to which datais being transferred).

In other cases, the data model extractor operator 1510 can specifyunstructured data to be retrieved, including options similar to thoseused for structured data. For example, the data model extractor operator1510 can specify particular locations for unstructured data to betransferred, particular file types or metadata properties ofunstructured data that is requested, a package size for transfer, and aschedule at which to receive updated data or to otherwise refresh therelevant data (e.g., transferring all of the requested data, rather thatspecifically identifying changed unstructured data).

Typically, the type of data model extractor operator 1510 is selectedbased on the nature of a particular machine learning scenario, includingthe particular algorithm being used. In many cases, machine learningalgorithms are configured to use either structured data or unstructureddata, at least for a given scenario. However, a given machine learningextraction pipeline can include a data model extractor operator 1510that requests both structured and unstructured data, or can includemultiple data model extractor operators (e.g., an operator forstructured data and another operator for unstructured data).

The machine learning pipeline 1500 can further include one or more datapreprocessing operators 1520. A data preprocessing operator 1520 can beused to prepare data for use by a machine learning algorithm operator1530. The data preprocessing operator 1520 can perform actions such asformatting data, labelling data, checking data integrity or suitability(e.g., a minimum number of data points), calculating additional values,or determining parameters to be used with the machine learning algorithmoperator 1530.

The machine learning algorithm operator 1530 is a particular machinelearning algorithm that is used to process data received and processedin the machine learning pipeline 1500. The machine learning algorithmoperator 1530 can include configuration information for particularparameters to be used for a particular scenario of interest, and caninclude configuration information for particular output that is desired(including data visualization information or other information used tointerpret machine learning results).

The machine learning pipeline 1500 includes a machine learning modeloperator 1540 that represents the machine learning model produced bytraining the machine learning algorithm associated with the machinelearning algorithm operator 1530. The machine learning model operator1540 represents the actual model that can be used to provide machinelearning results.

Typically, once the machine learning pipeline 1500 has been executedsuch that the operators 1510, 1520, 1530 have completed, a user can callthe machine learning model operator 1540 to obtain results for aparticular scenario (e.g., a set of input data). Unless it is desired toupdate or retrain the corresponding algorithm, it is not necessary toexecute other operators in the machine learning pipeline 1500,particularly operations associated with the data model extractoroperator 1510.

Example 11—Example Machine Learning Scenario Definition

FIG. 16 illustrates example metadata 1600 that can be stored as part ofa machine learning scenario. The machine learning scenario can representa machine learning scenario of the type configurable using the userinterface screens shown in FIGS. 6-11 , or a scenario 200, 300 depictedin FIGS. 2 and 3 . Information in a machine learning scenario can beused to execute various aspects of the scenario, such as training amachine learning model (including a model segment) or using the model toprocess a particular set of input data.

The metadata 1600 can include a scenario ID 1604 useable to uniquelyidentify a scenario. A more semantically meaningful name 1608 can beassociated with a given scenario ID 1604, although the name 1608 may notbe constrained to be unique. In some cases, the scenario ID 1604 can beused as the identifier for a particular subscriber to structured orunstructured data. A particular client (e.g., system or end user) 1612can be included in the metadata 1600.

An identifier 1616 can indicate a particular machine learning algorithmto be used for a given scenario, and can include a location 1618 forwhere the algorithm can be accessed. A target identifier 1622 can beused to indicate a location 1624 where a trained model should be stored.When the trained model is to be used, results are typically processed toprovide particular information (including as part of a visualization) toan end user. Information useable to process results of using a machinelearning algorithm for a particular set of input can be specified in ametadata element 1626, including a location 1628.

As discussed in prior Examples, a machine learning scenario can beassociated with a particular machine learning pipeline, such as themachine learning pipeline 1500 of FIG. 15 . An identifier of thepipeline can be specified by a metadata element 1630, and a location forthe pipeline (e.g., a definition of the pipeline) can be specified by ametadata element 1632. Optionally, particular operators in the givenmachine learning pipeline can be specified by metadata elements 1636,with locations of the operators provided by metadata elements 1638.

In a similar manner, the metadata 1600 can include elements 1642 thatspecify particular virtual data model artefacts that are included in themachine learning scenario, and elements 1644 that specify a location forthe respective virtual data model artefact. In other cases, the metadata1600 does not include the elements 1642, 1644, and virtual data modelartefacts can be obtained using, for example, a definition for apipeline operator. While not shown, the metadata 1600 could includeinformation for unstructured data used by the machine learning scenario,or such information could be stored in a definition for a pipelineoperator associated with unstructured data.

Example 12—Example Relationship Between Elements of a Database Schema

In some cases, data model information can be stored in a data dictionaryor similar repository, such as an information schema. An informationschema can store information defining an overall data model or schema,tables in the schema, attributes in the tables, and relationshipsbetween tables and attributes thereof. However, data model informationcan include additional types of information, as shown in FIG. 17 .

FIG. 17 is a diagram illustrating elements of a database schema 1700 andhow they can be interrelated. In at least some cases, the databaseschema 1700 can be maintained other than at the database layer of adatabase system. That is, for example, the database schema 1700 can beindependent of the underlying database, including a schema used for theunderlying database. Typically, the database schema 1700 is mapped to aschema of the database layer, such that records, or portions thereof(e.g., particular values of particular fields) can be retrieved throughthe database schema 1700.

The database schema 1700 can include one or more packages 1710. Apackage 1710 can represent an organizational component used tocategorize or classify other elements of the schema 1700. For example,the package 1710 can be replicated or deployed to various databasesystems. The package 1710 can also be used to enforce securityrestrictions, such as by restricting access of particular users orparticular applications to particular schema elements.

A package 1710 can be associated with one or more domains 1714 (i.e., aparticular type of semantic identifier or semantic information). Inturn, a domain 1714 can be associated with one or more packages 1710.For instance, domain 1, 1714 a, is associated only with package 1710 a,while domain 2, 1714 b, is associated with package 1710 a and package1710 b. In at least some cases, a domain 1714 can specify which packages1710 may use the domain. For instance, it may be that a domain 1714associated with materials used in a manufacturing process can be used bya process-control application, but not by a human resources application.

In at least some implementations, although multiple packages 1710 canaccess a domain 1714 (and database objects that incorporate the domain),a domain (and optionally other database objects, such as tables 1718,data elements 1722, and fields 1726, described in more detail below) isprimarily assigned to one package. Assigning a domain 1714, and otherdatabase objects, to a unique package can help create logical (orsemantic) relationships between database objects. In FIG. 17 , anassignment of a domain 1714 to a package 1710 is shown as a solid line,while an access permission is shown as a dashed line. So, domain 1714 ais assigned to package 1710 a, and domain 1714 b is assigned to package1710 b. Package 1710 a can access domain 1714 b, but package 1710 bcannot access domain 1714 a.

Note that at least certain database objects, such as tables 1718, caninclude database objects that are associated with multiple packages. Forexample, a table 1718, Table 1, may be assigned to package A, and havefields that are assigned to package A, package B, and package C. The useof fields assigned to packages A, B, and C in Table 1 creates a semanticrelationship between package A and packages B and C, which semanticrelationship can be further explained if the fields are associated withparticular domains 1714 (that is, the domains can provide furthersemantic context for database objects that are associated with an objectof another package, rather than being assigned to a common package).

As will be explained in more detail, a domain 1714 can represent themost granular unit from which database tables 1718 or other schemaelements or objects can be constructed. For instance, a domain 1714 mayat least be associated with a datatype. Each domain 1714 is associatedwith a unique name or identifier, and is typically associated with adescription, such as a human readable textual description (or anidentifier than can be correlated with a human readable textualdescription) providing the semantic meaning of the domain. For instance,one domain 1714 can be an integer value representing a phone number,while another domain can be an integer value representing a part number,while yet another integer domain may represent a social security number.The domain 1714 thus can held provide common and consistent use (e.g.,semantic meaning) across the schema 1700. That is, for example, whenevera domain representing a social security number is used, thecorresponding fields can be recognized as having this meaning even ifthe fields or data elements have different identifiers or othercharacteristics for different tables.

The schema 1700 can include one or more data elements 1722. Each dataelement 1722 is typically associated with a single domain 1714. However,multiple data elements 1722 can be associated with a particular domain1714. Although not shown, multiple elements of a table 1718 can beassociated with the same data element 1722, or can be associated withdifferent data elements having the same domain 1714. Data elements 1722can serve, among other things, to allow a domain 1714 to be customizedfor a particular table 1718. Thus, the data elements 1722 can provideadditional semantic information for an element of a table 1718.

Tables 1718 include one or more fields 1726, at least a portion of whichare mapped to data elements 1722. The fields 1726 can be mapped to aschema of a database layer, or the tables 1718 can be mapped to adatabase layer in another manner In any case, in some embodiments, thefields 1726 are mapped to a database layer in some manner Or, a databaseschema can include semantic information equivalent to elements of theschema 1700, including the domains 1714.

In some embodiments, one or more of the fields 1726 are not mapped to adomain 1714. For example, the fields 1726 can be associated withprimitive data components (e.g., primitive datatypes, such as integers,strings, Boolean values, character arrays, etc.), where the primitivedata components do not include semantic information. Or, a databasesystem can include one or more tables 1718 that do not include anyfields 1726 that are associated with a domain 1714. However, thedisclosed technologies include a schema 1700 (which can be separatefrom, or incorporated into, a database schema) that includes a pluralityof tables 1718 having at least one field 1726 that is associated with adomain 1714, directly or through a data element 1722.

Example 13—Example Data Dictionary

Schema information, such as information associated with the schema 1700of FIG. 17 , can be stored in a repository, such as a data dictionary.As discussed, in at least some cases the data dictionary is independentof, but mapped to, an underlying relational database. Such independencecan allow the same database schema 1700 to be mapped to differentunderlying databases (e.g., databases using software from differentvendors, or different software versions or products from the samevendor). The data dictionary can be persisted, such as being maintainedin a stored tables, and can be maintained in memory, either in whole orpart. An in-memory version of a data dictionary can be referred to as adictionary buffer.

FIG. 18 illustrates a database environment 1800 having a data dictionary1804 that can access, such as through a mapping, a database layer 1808.The database layer 1808 can include a schema 1812 (e.g., anINFORMATION_SCHEMA as in PostgreSQL) and data 1816, such as dataassociated with tables 1818. The schema 1812 includes various technicaldata items/components 1822, which can be associated with a field 1820,such as a field name 1822 a (which may or may not correspond to areadily human-understandable description of the purpose of the field, orotherwise explicitly describe the semantic meaning of values for thatfield), a field data type 1822 b (e.g., integer, varchar, string,Boolean), a length 1822 c (e.g., the size of a number, the length of astring, etc., allowed for values in the field), a number of decimalplaces 1822 d (optionally, for suitable datatypes, such as, for a floatwith length 6, specifying whether the values represent XX.XXXX orXXX.XXX), a position 1822 e (e.g., a position in the table where thefield should be displayed, such as being the first displayed field, thesecond displayed field, etc.), optionally, a default value 1822 f (e.g.,“NULL,” “0,” or some other value), a NULL flag 1822 g indicating whetherNULL values are allowed for the field, a primary key flag 1822 hindicating whether the field is, or is used in, a primary key for thetable, and a foreign key element 1822 i, which can indicate whether thefield 1820 is associated with a primary key of another table, and,optionally, an identifier of the table/field referenced by the foreignkey element. A particular schema 1812 can include more, fewer, ordifferent technical data items 1822 than shown in FIG. 18 .

The tables 1818 are associated with one or more values 1826. The values1826 are typically associated with a field 1820 defined using one ormore of the technical data elements 1822. That is, each row 1828typically represents a unique tuple or record, and each column 1830 istypically associated with a definition of a particular field 1820. Atable 1818 typically is defined as a collection of the fields 1820, andis given a unique identifier.

The data dictionary 1804 includes one or more packages 1834, one or moredomains 1838, one or more data elements 1842, and one or more tables1846, which can at least generally correspond to the similarly titledcomponents 1710, 1714, 1722, 1718, respectively, of FIG. 17 . Asexplained in the discussion of FIG. 17 , a package 1834 includes one ormore (typically a plurality) of domains 1838. Each domain 1838 isdefined by a plurality of domain elements 1840. The domain elements 1840can include one or more names 1840 a. The names 1840 a serve toidentify, in some cases uniquely, a particular domain 1838. A domain1838 includes at least one unique name 1840 a, and may include one ormore names that may or may not be unique. Names which may or may not beunique can include versions of a name, or a description, of the domain1838 at various lengths or levels of detail. For instance, names 1840 acan include text that can be used as a label for the domain 1838, andcan include short, medium, and long versions, as well as text that canbe specified as a heading. Or, the names 1840 a can include a primaryname or identifier and a short description or field label that provideshuman understandable semantics for the domain 1838.

In at least some cases, the data dictionary 1804 can store at least aportion of the names 1840 a in multiple languages, such as having domainlabels available for multiple languages. In embodiments of the disclosedtechnologies, when domain information is used for identifyingrelationships between tables or other database elements or objects,including searching for particular values, information, such as names1840 a, in multiple languages can be searched. For instance, if“customer” is specified, the German and French portion of the names 1840a can be searched as well as an English version.

The domain elements 1840 can also include information that is at leastsimilar to information that can be included in the schema 1812. Forexample, the domain elements 1840 can include a data type 1840 b, alength 1840 c, and a number of decimal places 1840 d associated withrelevant data types, which can correspond to the technical data elements1822 b, 1822 c, 1822 d, respectively. The domain elements 1840 caninclude conversion information 1840 e. The conversion information 1840 ecan be used to convert (or interconvert) values entered for the domain1838 (including, optionally, as modified by a data element 1842). Forinstance, conversion information 1840 can specify that a number havingthe form XXXXXXXXX should be converted to XXX-XX-XXXX, or that a numbershould have decimals or comma separating various groups of numbers(e.g., formatting 1234567 as 1,234,567.00). In some cases, fieldconversion information for multiple domains 1838 can be stored in arepository, such as a field catalog.

The domain elements 1840 can include one or more value restrictions 1840f. A value restriction 1840 f can specify, for example, that negativevalues are or are not allowed, or particular ranges or threshold ofvalues that are acceptable for a domain 1838. In some cases, an errormessage or similar indication can be provided as a value is attempted tobe used with a domain 1838 that does not comply with a value restriction1840 f. A domain element 1840 g can specify one or more packages 1834that are allowed to use the domain 1838.

A domain element 1840 h can specify metadata that records creation ormodification events associated with a domain element 1838. For instance,the domain element 1840 h can record the identity of a user orapplication that last modified the domain element 1840 h, and a timethat the modification occurred. In some cases, the domain element 1840 hstores a larger history, including a complete history, of creation andmodification of a domain 1838.

A domain element 1840 i can specify an original language associated witha domain 1838, including the names 1840 a. The domain element 1840 i canbe useful, for example, when it is to be determined whether the names1840 a should be converted to another language, or how such conversionshould be accomplished.

Data elements 1842 can include data element fields 1844, at least someof which can be at least generally similar to domain elements 1840. Forexample, a data element field 1844 a can correspond to at least aportion of the name domain element 1840 a, such as being (or including)a unique identifier of a particular data element 1842. The field labelinformation described with respect to the name domain element 1840 a isshown as separated into a short description label 1844 b, a mediumdescription label 1844 c, a long description label 1844 d, and a headerdescription 1844 e. As described for the name domain element 1840 a, thelabels and header 1844 b-1844 e can be maintained in one language or inmultiple languages.

A data element field 1844 f can specify a domain 1838 that is used withthe data element 1842, thus incorporating the features of the domainelements 1840 into the data element. Data element field 1844 g canrepresent a default value for the data element 1842, and can be at leastanalogous to the default value 1822 f of the schema 1812. Acreated/modified data element field 1844 h can be at least generallysimilar to the domain element 1840 h.

Tables 1846 can include one or more table elements 1848. At least aportion of the table elements 1848 can be at least similar to domainelements 1840, such as table element 1848 a being at least generallysimilar to domain element 1840 a, or data element field 1844 a. Adescription table element 1848 b can be analogous to the description andheader labels described in conjunction with the domain element 1840 a,or the labels and header data element fields 1844 b-1844 e. A table 1846can be associated with a type using table element 1848 c. Example tabletypes include transparent tables, cluster tables, and pooled tables,such as used as in database products available from SAP SE of Walldorf,Germany.

Tables 1846 can include one or more field table elements 1848 d. A fieldtable element 1848 d can define a particular field of a particulardatabase table. Each field table element 1848 d can include anidentifier 1850 a of a particular data element 1842 used for the field.Identifiers 1850 b-1850 d, can specify whether the field is, or is partof, a primary key for the table (identifier 1850 b), or has arelationship with one or more fields of another database table, such asbeing a foreign key (identifier 1850 c) or an association (identifier1850 d).

A created/modified table element 1848 e can be at least generallysimilar to the domain element 1840 h.

Example 14—Computing Systems

FIG. 19 depicts a generalized example of a suitable computing system1900 in which the described innovations may be implemented. Thecomputing system 1900 is not intended to suggest any limitation as toscope of use or functionality of the present disclosure, as theinnovations may be implemented in diverse general-purpose orspecial-purpose computing systems.

With reference to FIG. 19 , the computing system 1900 includes one ormore processing units 1910, 1915 and memory 1920, 1925. In FIG. 19 ,this basic configuration 1930 is included within a dashed line. Theprocessing units 1910, 1915 execute computer-executable instructions,such as for implementing technologies described in any of Examples 1-13A processing unit can be a general-purpose central processing unit(CPU), processor in an application-specific integrated circuit (ASIC),or any other type of processor. In a multi-processing system, multipleprocessing units execute computer-executable instructions to increaseprocessing power. For example, FIG. 19 shows a central processing unit1910 as well as a graphics processing unit or co-processing unit 1915.The tangible memory 1920, 1925 may be volatile memory (e.g., registers,cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory,etc.), or some combination of the two, accessible by the processingunit(s) 1910, 1915. The memory 1920, 1925 stores software 1980implementing one or more innovations described herein, in the form ofcomputer-executable instructions suitable for execution by theprocessing unit(s) 1910, 1915.

A computing system 1900 may have additional features. For example, thecomputing system 1900 includes storage 1940, one or more input devices1950, one or more output devices 1960, and one or more communicationconnections 1970. An interconnection mechanism (not shown) such as abus, controller, or network interconnects the components of thecomputing system 1900. Typically, operating system software (not shown)provides an operating environment for other software executing in thecomputing system 1900, and coordinates activities of the components ofthe computing system 1900.

The tangible storage 1940 may be removable or non-removable, andincludes magnetic disks, magnetic tapes or cassettes, CD-ROMs, DVDs, orany other medium which can be used to store information in anon-transitory way and which can be accessed within the computing system1900. The storage 1940 stores instructions for the software 1980implementing one or more innovations described herein.

The input device(s) 1950 may be a touch input device such as a keyboard,mouse, pen, or trackball, a voice input device, a scanning device, oranother device that provides input to the computing system 1900. Theoutput device(s) 1960 may be a display, printer, speaker, CD-writer, oranother device that provides output from the computing system 1900.

The communication connection(s) 1970 enable communication over acommunication medium to another computing entity. The communicationmedium conveys information such as computer-executable instructions,audio or video input or output, or other data in a modulated datasignal. A modulated data signal is a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia can use an electrical, optical, RF, or other carrier.

The innovations can be described in the general context ofcomputer-executable instructions, such as those included in programmodules, being executed in a computing system on a target real orvirtual processor. Generally, program modules or components includeroutines, programs, libraries, objects, classes, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. The functionality of the program modules may becombined or split between program modules as desired in variousembodiments. Computer-executable instructions for program modules may beexecuted within a local or distributed computing system.

The terms “system” and “device” are used interchangeably herein. Unlessthe context clearly indicates otherwise, neither term implies anylimitation on a type of computing system or computing device. Ingeneral, a computing system or computing device can be local ordistributed, and can include any combination of special-purpose hardwareand/or general-purpose hardware with software implementing thefunctionality described herein.

In various examples described herein, a module (e.g., component orengine) can be “coded” to perform certain operations or provide certainfunctionality, indicating that computer-executable instructions for themodule can be executed to perform such operations, cause such operationsto be performed, or to otherwise provide such functionality. Althoughfunctionality described with respect to a software component, module, orengine can be carried out as a discrete software unit (e.g., program,function, class method), it need not be implemented as a discrete unit.That is, the functionality can be incorporated into a larger or moregeneral-purpose program, such as one or more lines of code in a largeror general-purpose program.

For the sake of presentation, the detailed description uses terms like“determine” and “use” to describe computer operations in a computingsystem. These terms are high-level abstractions for operations performedby a computer, and should not be confused with acts performed by a humanbeing. The actual computer operations corresponding to these terms varydepending on implementation.

Example 15—Cloud Computing Environment

FIG. 20 depicts an example cloud computing environment 2000 in which thedescribed technologies can be implemented, such as a cloud system 114 ofFIG. 1 . The cloud computing environment 2000 comprises cloud computingservices 2010. The cloud computing services 2010 can comprise varioustypes of cloud computing resources, such as computer servers, datastorage repositories, networking resources, etc. The cloud computingservices 2010 can be centrally located (e.g., provided by a data centerof a business or organization) or distributed (e.g., provided by variouscomputing resources located at different locations, such as differentdata centers and/or located in different cities or countries).

The cloud computing services 2010 are utilized by various types ofcomputing devices (e.g., client computing devices), such as computingdevices 2020, 2022, and 2024. For example, the computing devices (e.g.,2020, 2022, and 2024) can be computers (e.g., desktop or laptopcomputers), mobile devices (e.g., tablet computers or smart phones), orother types of computing devices. For example, the computing devices(e.g., 2020, 2022, and 2024) can utilize the cloud computing services2010 to perform computing operators (e.g., data processing, datastorage, and the like). The computing devices 2020, 2022, 2024 cancorrespond to the local system 110 FIG. 1 , or can represent a clientdevice, such as a client 116, 118.

Example 16—Implementations

Although the operations of some of the disclosed methods are describedin a particular, sequential order for convenient presentation, it shouldbe understood that this manner of description encompasses rearrangement,unless a particular ordering is required by specific language set forthbelow. For example, operations described sequentially may in some casesbe rearranged or performed concurrently. Moreover, for the sake ofsimplicity, the attached figures may not show the various ways in whichthe disclosed methods can be used in conjunction with other methods.

Any of the disclosed methods can be implemented as computer-executableinstructions or a computer program product stored on one or morecomputer-readable storage media, such as tangible, non-transitorycomputer-readable storage media, and executed on a computing device(e.g., any available computing device, including smart phones or othermobile devices that include computing hardware). Tangiblecomputer-readable storage media are any available tangible media thatcan be accessed within a computing environment (e.g., one or moreoptical media discs such as DVD or CD, volatile memory components (suchas DRAM or SRAM), or nonvolatile memory components (such as flash memoryor hard drives)). By way of example, and with reference to FIG. 19 ,computer-readable storage media include memory 1920 and 1925, andstorage 1940. The term computer-readable storage media does not includesignals and carrier waves. In addition, the term computer-readablestorage media does not include communication connections (e.g., 1970).

Any of the computer-executable instructions for implementing thedisclosed techniques as well as any data created and used duringimplementation of the disclosed embodiments can be stored on one or morecomputer-readable storage media. The computer-executable instructionscan be part of, for example, a dedicated software application or asoftware application that is accessed or downloaded via a web browser orother software application (such as a remote computing application).Such software can be executed, for example, on a single local computer(e.g., any suitable commercially available computer) or in a networkenvironment (e.g., via the Internet, a wide-area network, a local-areanetwork, a client-server network (such as a cloud computing network), orother such network) using one or more network computers.

For clarity, only certain selected aspects of the software-basedimplementations are described. It should be understood that thedisclosed technology is not limited to any specific computer language orprogram. For instance, the disclosed technology can be implemented bysoftware written in C, C++, C#, Java, Perl, JavaScript, Python, Ruby,ABAP, SQL, XCode, GO, Adobe Flash, or any other suitable programminglanguage, or, in some examples, markup languages such as html or XML, orcombinations of suitable programming languages and markup languages.Likewise, the disclosed technology is not limited to any particularcomputer or type of hardware.

Furthermore, any of the software-based embodiments (comprising, forexample, computer-executable instructions for causing a computer toperform any of the disclosed methods) can be uploaded, downloaded, orremotely accessed through a suitable communication means. Such suitablecommunication means include, for example, the Internet, the World WideWeb, an intranet, software applications, cable (including fiber opticcable), magnetic communications, electromagnetic communications(including RF, microwave, and infrared communications), electroniccommunications, or other such communication means.

The disclosed methods, apparatus, and systems should not be construed aslimiting in any way. Instead, the present disclosure is directed towardall novel and nonobvious features and aspects of the various disclosedembodiments, alone and in various combinations and sub combinations withone another. The disclosed methods, apparatus, and systems are notlimited to any specific aspect or feature or combination thereof, nor dothe disclosed embodiments require that any one or more specificadvantages be present, or problems be solved.

The technologies from any example can be combined with the technologiesdescribed in any one or more of the other examples. In view of the manypossible embodiments to which the principles of the disclosed technologymay be applied, it should be recognized that the illustrated embodimentsare examples of the disclosed technology and should not be taken as alimitation on the scope of the disclosed technology. Rather, the scopeof the disclosed technology includes what is covered by the scope andspirit of the following claims

What is claimed is:
 1. A computing system comprising: at least onehardware processor; at least one memory coupled to the at least onehardware processor; and one or more computer-readable storage mediastoring computer-executable instructions that, when executed, cause thecomputing system to perform operations comprising: receiving a requestfor a machine learning result; determining a machine learning scenarioassociated with the request; determining at least one value for at leastone hyperparameter for a machine learning algorithm associated with themachine learning scenario; configuring the machine learning algorithmwith the at least one value; determining at least one filter valuespecified in the request; determining a model segment of a plurality ofmodel segments to be used in processing the request, based at least inpart on the at least one filter value; and generating a machine learningresult using the model segment configured with the at least one filtervalue.
 2. The computing system of claim 1, the operations furthercomprising: determining that the model segment should be retrained; andretraining the model segment.
 3. The computing system of claim 2,wherein the determining that the model segment should be retrained isbased on a schedule.
 4. The computing system of claim 1, the operationsfurther comprising: determining at least one configuration settingassociated with the machine learning scenario; and applying the at leastone configuration setting to the machine learning model.
 5. Thecomputing system of claim 4, wherein the generating a machine learningresult is carried out by a first computing system, the operationsfurther comprising: receiving the at least one configuration settingfrom a second computing system.
 6. The computing system of claim 1, theoperations further comprising: receiving user input specifying the atleast one value for the at least one hyperparameter; and storing the atleast one value in association with the machine learning scenario. 7.The computing system of claim 6, the operations further comprising:receiving a second request for a machine learning result using a secondmachine learning scenario; determining that a value for the at least onehyperparameter was not specified for the second machine learningscenario; configuring the machine learning algorithm with a defaultvalue for the at least one hyperparameter; and generating a secondmachine learning result using the machine learning algorithm configuredwith the default second value.
 8. A method, implemented in a computingsystem comprising at least one hardware processor and at least onememory coupled to the at least one hardware processor, the methodcomprising: receiving a request for a machine learning result;determining a machine learning scenario associated with the request;determining at least one value for at least one hyperparameter for amachine learning algorithm associated with the machine learningscenario; configuring the machine learning algorithm with the at leastone value; determining at least one filter value specified in therequest; determining a model segment of a plurality of model segments tobe used in processing the request, based at least in part on the atleast one filter value; and generating a machine learning result usingthe model segment configured with the at least one filter value.
 9. Themethod of claim 8, further comprising: determining that the modelsegment should be retrained; and retraining the model segment.
 10. Themethod of claim 9, wherein the determining that the model segment shouldbe retrained is based on a schedule.
 11. The method of claim 8, furthercomprising: determining at least one configuration setting associatedwith the machine learning scenario; and applying the at least oneconfiguration setting to the machine learning model.
 12. The method ofclaim 11, wherein the generating a machine learning result is carriedout by a first computing system, the method further comprising:receiving the at least one configuration setting from a second computingsystem.
 13. The method of claim 8, further comprising: receiving userinput specifying the at least one value for the at least onehyperparameter; and storing the at least one value in association withthe machine learning scenario.
 14. The method of claim 13, furthercomprising: receiving a second request for a machine learning resultusing a second machine learning scenario; determining that a value forthe at least one hyperparameter was not specified for the second machinelearning scenario; configuring the machine learning algorithm with adefault value for the at least one hyperparameter; and generating asecond machine learning result using the machine learning algorithmconfigured with the default value.
 15. One or more computer-readablestorage media comprising: computer-executable instructions that, whenexecuted by a computing system comprising at least one hardwareprocessor and at least one memory coupled to the at least one hardwareprocessor, cause the computing system to receive a request for a machinelearning result; computer-executable instructions that, when executed bythe computing system, cause the computing system to determine a machinelearning scenario associated with the request; computer-executableinstructions that, when executed by the computing system, cause thecomputing system to determine at least one value for at least onehyperparameter for a machine learning algorithm associated with themachine learning scenario; computer-executable instructions that, whenexecuted by the computing system, cause the computing system toconfigure the machine learning algorithm with the at least one value;computer-executable instructions that, when executed, cause a computingdevice to determine at least one filter value specified in the request;computer-executable instructions that, when executed by the computingsystem, cause the computing system to determine a model segment of aplurality of model segments to be used in processing the request, basedat least in part on the at least one filter value; andcomputer-executable instructions that, when executed by the computingsystem, cause the computing system to generate a machine learning resultusing the model segment configured with the at least one filter value.16. The one or more computer-readable storage media of claim 15, furthercomprising: computer-executable instructions that, when executed by thecomputing system, cause the computing system to determine that the modelsegment should be retrained; and computer-executable instructions that,when executed by the computing system, cause the computing system toretrain the model segment.
 17. The one or more computer-readable storagemedia of claim 16, wherein the determining that the model segment shouldbe retrained is based on a schedule.
 18. The one or morecomputer-readable storage media of claim 15, further comprising:computer-executable instructions that, when executed by the computingsystem, cause the computing system to determine at least oneconfiguration setting associated with the machine learning scenario; andcomputer-executable instructions that, when executed by the computingsystem, cause the computing system to apply the at least oneconfiguration setting to the machine learning model.
 19. The one or morecomputer-readable storage media of claim 18, wherein the generating amachine learning result is carried out by a first computing system, theone or more computer-readable storage media further comprising:computer-executable instructions that, when executed by the computingsystem, cause the computing system to receive the at least oneconfiguration setting from a second computing system.
 20. The one ormore computer-readable storage media of claim 15, further comprising:computer-executable instructions that, when executed by the computingsystem, cause the computing system to receive user input specifying theat least one value for the at least one hyperparameter; andcomputer-executable instructions that, when executed by the computingsystem, cause the computing system to store the at least one value inassociation with the machine learning scenario.